• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9B
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• pp.604-611
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• 2007

In this paper, we present an analysis of the throughput when there are multiple piconets and WLAN sharing the ISM bands. The analysis takes channel propagation characteristics and the capture effect. We also propose an algorithm which can be used to reduce the amount of channel scanning. By using traffic prediction of the interfering WLAN, we are able to maintain a reasonable performance in terms of fraction of time channel is wasted due to collisions or unused channel. Through computer simulation, we demonstrate that the proposed algorithm achieves reduced scanning frequency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7B
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• pp.469-476
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• 2008

In this paper we analyze the packet interference probability and the aggregated throughput of a WPAN in which a number of Bluetooth piconets share the ISM band with WLANS. Using an Adaptive Frequency Hopping algorithm, when the AFH is employed, the number of hops available to the Bluetooth piconets varies depending on the number of independent WLANs within the piconet's radio range. Using a packet collision model in a piconet cluster, we give an analysis of the packet interference probability and the aggregated throughput as a function of the available hops for the AFH algorithm. We also present an analytical model of packet interference with multi-path fading channel in a cluster of piconets. Through analysis, we obtain the packet collision probability and aggregated throughput assuming capture effect. Numerical examples are given to demonstrate the effect of various Parameters such as capture ratio, Rice factor and cluster size on the system performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.23-34
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• 2004

In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.